Information from sensors is the base point in the optimization of traffic management and law enforcement. Using sensors allows gathering statistical data about different parameters related to traffic monitoring and detecting traffic infractions like speed limit violations. Examples of interesting parameters to track are detecting the presence of a vehicle in a detection zone, counting the number of vehicles on the roadway, namely the volume on the roadway, determining the lane position, classifying the vehicle, counting the number of axles, determining the direction of the vehicle, estimating the occupancy and determining the speed.
In the case of speed enforcement, especially for average speed enforcement, determining the exact position of the front and back of a vehicle is useful data. Average speed measurement systems measure the average speed of a vehicle over a predetermined distance and use detectors to determine the time at the entry and the exit points of one section of a vehicle. The entry and exit points are usually hundreds of meters or even kilometers apart. Then, they synchronize the automatic plate number recognition and vehicle identification systems and use the known distance between those points to calculate the average speed of a vehicle. In the case of an average speed exceeding the speed limit, a fine can be issued by law enforcement authorities.
Speed enforcement can require classifying vehicles to determine the right speed limit for a vehicle type. Some countries set different minimum and/or maximum speed limits for heavy trucks and buses. Commercial vehicles can also have other constraints such as truck lane restrictions specifying on which lane a certain type of vehicle is allowed to travel, to requiring classification functionality from the detection system.
Advanced Transportation Management Systems (ATMS) rely on accurate traffic data from different kinds of detectors divided in two categories: intrusive and non-intrusive. One type of intrusive detectors involves inductive loop detectors that are still a common technology for detecting vehicles even if that technology has some disadvantages such as lengthy disruption to the traffic flow during installation and maintenance, relatively high cost, high failure rate and inflexibility. Other detectors, like cameras with video processing, radar-based sensors, laser-based sensors, passive infrared and ultrasound sensors have been introduced for traffic monitoring but also have their limitations and the market is still searching for alternatives.
Video processing sensors have well know drawbacks such as the lack of performance in terms of false alarms during night operation or the difficulty to perform during bad weather conditions affecting visibility such as during an episode of fog. Environmental particles are known to be difficult to manage.
Radar technology is known to perform well in bad weather conditions but has some limitations in terms of lateral resolution. Accurate occupancy measurement can be limited when occupancy is high. In some cases, for measuring the speed of a vehicle, radar traffic detectors located on the side of the road use an average length for the vehicles which causes errors in the vehicle speed estimate.
Thus, there is a need for a method and system for robust and accurate detection for multipurpose traffic management applications.